AI Data Analyst

As an experienced AI Data Analyst with 7+ years of professional experience, you will be responsible for working on cutting-edge AI solutions, including vector-based search and data-driven business insights. Your key responsibilities will include: - Performing data exploration, profiling, and cleaning across large datasets. - Designing, implementing, and evaluating machine learning and AI models for business problems. - Leveraging LLM APIs, foundation models, and vector databases to power AI-driven analysis. - Building end-to-end ML workflows from data preprocessing to deployment. - Developing visualizations and dashboards for internal reports and presentations. - Analyzing and interpreting model outputs, providing actionable insights to stakeholders. - Collaborating with engineering and product teams to scale AI solutions across business processes. Qualifications required for this role include: - 2+ year of hands-on experience as a Data Analyst. - 1+ year of practical experience with AI systems (LLMs, AI APIs, or vector-based search). - 2+ years of experience working with Machine Learning models/solutions. - 5+ years of hands-on Python programming. - Exposure to vector databases (e.g., pgvector, ChromaDB) is a plus. In terms of skills, you should have: Data Analysis: - 1+ year of hands-on experience working with real-world datasets. - Strong Exploratory Data Analysis (EDA), data wrangling, and visualization using tools like Pandas, Seaborn, or Plotly. Machine Learning & AI: - 2+ years of experience applying machine learning techniques (classification, regression, clustering, etc.). - 1+ year of hands-on experience with AI technologies, including Generative AI, LLMs, AI APIs (e.g., OpenAI, Hugging Face), and vector-based search systems. - Familiarity with model evaluation, hyperparameter tuning, and model selection. - Exposure to AI-driven analysis, including RAG (Retrieval-Augmented Generation) and other AI solution architectures. Programming: - 3+ years of Python programming experience, with proficiency in libraries like scikit-learn, NumPy, Pandas, etc. - Strong understanding of data structures and algorithms relevant to AI and ML. Tools & Technologies: - SQL/PostgreSQL proficiency. - Experience with vector databases (e.g., pgvector, ChromaDB). - Exposure to LLMs, foundation models, RAG systems, and embedding techniques. - Familiarity with AWS, SageMaker, or similar cloud platforms. - Version control systems knowledge (e.g., Git), REST APIs, and Linux. Good to have skills include experience with Scrapy, SpaCy, or OpenCV, knowledge of MLOps, model deployment, and CI/CD pipelines, and familiarity with deep learning frameworks like PyTorch or TensorFlow. Soft skills required for this role are a strong problem-solving mindset, analytical thinking, excellent communication skills, capability of presenting technical information clearly to non-technical stakeholders, and being collaborative, proactive, and self-driven in a fast-paced, dynamic environment.,